| Name of the Table | Source |
|---|---|
| D1_1_SDG | dashboards.sdgindex.org |
| D2_2_Unemployment_rate | ilo.org |
| D3_0_GDP_per_capita | data.worldbank.org |
| D3_1_Military_expenditure_percent_GDP | data.worldbank.org |
| D3_2_Military_expenditure_percent_gov_exp | data.worldbank.org |
| D4_0_Internet_usage | ourworldindata.org |
| D5_0_Human_freedom_index | cato.org |
| D6_0_Disaters | kaggle.com |
| D7_0_COVID | github.com |
| D8_0_Conflicts | datacatalog.worldbank.org |
Data
Sources
We are collecting our Data from the sustainability development report (SDG), the international labour organization (ILOSTAT), the World Bank, Our world in data, the CATO institute, one from Kaggle (disasters: we couldn’t find relevant accessible information from somewhere else) and GitHub. We found different datasets containing useful information in relation with the SDGs. The details about these data and the links are presented in the next section. Utilizing the kableExtra package, we provide a comprehensive list and corresponding links to our sources, as outlined below:
Description
During the wrangling process, we added data to our table (D1_1_SDG) from different other datasets and match them based on the country code, and the year. The tables below show all the variables present in our 9 databases. We will then merge them to have our final table for the analysis.
D1_1_SDG
Our primary database focuses on the Sustainable Development Goals (SDG). Below is a table summarizing the key variables included:
| Variable Name | Explanation |
|---|---|
| code | Country code (ISO) |
| country | Country name |
| year | Year of the observation (2000-2022) |
| overallscore | Overall score on all 17 SDGs (the score are % of achievement of the goals determined by the UN based on several indicators) |
| goal1:goal17 | Score on each SDG except SDG 14 (16 variables) |
| population | Population of the country |
D2_2_Unemployment_rate
| Variable Name | Explanation |
|---|---|
| code | Country code (ISO) |
| country | Country name |
| year | Year of the observation (2000-2022) |
| unemployment.rate | Unemployment rate (% of the population 15 years old and older) |
D3_0_GDP_per_capita
| Variable Name | Explanation |
|---|---|
| code | Country code (ISO) |
| country | Country name |
| year | Year of the observation (2000-2022) |
| GDPpercapita | GDP per capita |
D3_1_Military_expenditure_percent_GDP
| Variable Name | Explanation |
|---|---|
| code | Country code (ISO) |
| country | Country name |
| year | Year of the observation (2000-2022) |
| MilitaryExpenditurePercentGDP | Military expenditures in percentage of GDP |
D4_0_Internet_usage
| Variable Name | Explanation |
|---|---|
| code | Country code (ISO) |
| country | Country name |
| year | Year of the observation (2000-2022) |
| internet.usage | Internet usage (% of the population) |
D5_0_Human_freedom_index
| Variable Name | Explanation |
|---|---|
| code | Country code (ISO) |
| country | Country name |
| year | Year of the observation (2000-2022) |
| region | Part of the world, group of countries (e.g. Eastern Europe, Dub-Saharan Africa, South Asia, etc.) |
| pf_law | Rule of law, mean score of: Procedural justice, Civil, justice, Criminal justice, Rule of law (V-Dem) |
| pf_security | Security and safety, mean score of: Homicide, Disappearances conflicts, terrorism |
| pf_movement | Freedom of movement (V-Dem), Freedom of movement (CLD) |
| pf_religion | Freedom of religion, Religious organization, repression |
| pf_assembly | Civil society entry and exit, Freedom of assembly, Freedom to form/run political parties, Civil society repression |
| pf_expression | Direct attacks on the press, Media and expression (V-Dem), Media and expression (Freedom House), Media and expression (BTI), Media and expression (CLD) |
| pf_identity | Same-sex relationships, Divorce, Inheritance rights, Female genital mutilation |
| ef_gouvernment | Government consumption, Transfers and subsidies, Government investment, Top marginal tax rate, State ownership of assets |
| ef_legal | Judicial independence, Impartial courts, Protection of property rights, Military interference Integrity of the legal system Legal enforcementof contracts, Regulatory costs, Reliability of police |
| ef_money | Money growth, Standard deviation of inflation, Inflation: Most recent year, Freedom to own foreign currency |
| ef_trade | Tariffs, Regulatory trade barriers, Black-market exchange rates, Movement of capital and people |
| ef_regulation | Credit market regulations, Labor market regulations, Business regulations |
D6_0_Disaters
| Variable Name | Explanation |
|---|---|
| code | Country code (ISO) |
| country | Country name |
| year | Year of the observation (2000-2022) |
| continent | Continents touched by the disasters such as floods, ouragan |
| total_deaths | Number of deaths caused by disasters |
| no_injured | Number of injured caused by disasters |
| no_affected | Number of affected caused by disasters |
| no_homeless | Number of homeless caused by disasters |
| total_affected | Total number of affected caused by disasters |
| total_damages | Total of infrastructure damages |
D7_0_COVID
| Variable Name | Explanation |
|---|---|
| code | Country code (ISO) |
| country | Country name |
| year | Year of the observation (2000-2022) |
| deaths_per_million | Number of people dead due to COVID |
| cases_per_million | Number of COVID cases |
| stringency | Government Response Stringency Index: composite measure based on 9 response indicators including school closures, workplace closures, and trave |
D8_0_Conflicts
| Variable Name | Explanation |
|---|---|
| code | Country code (ISO) |
| country | Country name |
| year | Year of the observation (2000-2022) |
| ongoing | Variable coded 1 for more than 25 deaths in intrastate conflict and 0 otherwise according to UCDP/PRIO Armed Conflict Dataset 17.1. |
| sum_deaths | Best estimate of deaths in all categories of violence (non-state, one-sided and state-based) recorded by the Uppsala Conflict Data Program in the country based on the UCDP GED dataset (unpublished 2016 data). The location of these events is used for estimating the extent of violence. |
| pop_affected | Share of population affected by violence in percentage (0 to 100) measured as described above based on population data from CIESIN, the PRIO-GRID structure as well as UCDP GED. |
| area_affected | Area affected by conflict |
| maxintensity | Two different intensity levels are coded: minor armed conflicts (1) and wars (2), Takes the max intensity of conflict in the country so that it is coded 2 if there is at least one war (>=1000 deaths in intrastate conflict) during the year. Data from UCDP/PRIO Armed Conflict Dataset 17.1. |
Wrangling/cleaning
To accommodate the large scale of the datasets, we pre-cleaned each one prior to merging. This streamlined the process, simplifying the cleaning of the final, combined dataset.
Dataset on SDG
This is our main dataset, that we clean in order to keep the columns containing the following information: country name, country code, year, population, overall score and the SDGs scores.
We start by importing the data and converting it into a DataFrame. Next, we rename the columns and convert the scores into numeric variables.
D1_0_SDG <- read.csv(here("scripts","data","SDG.csv"), sep = ";")
D1_0_SDG <- as.data.frame(D1_0_SDG)
D1_0_SDG <- D1_0_SDG[,1:22]
colnames(D1_0_SDG) <- c("code", "country", "year", "population", "overallscore", "goal1", "goal2", "goal3", "goal4", "goal5", "goal6", "goal7", "goal8", "goal9", "goal10", "goal11", "goal12", "goal13", "goal14", "goal15", "goal16", "goal17")
D1_0_SDG[["overallscore"]] <- as.double(gsub(",", ".", D1_0_SDG[["overallscore"]]))
makenumSDG <- function(D1_0_SDG) {
for (i in 1:17) {
varname <- paste("goal", i, sep = "")
D1_0_SDG[[varname]] <- as.double(gsub(",", ".", D1_0_SDG[[varname]]))
}
return(D1_0_SDG)
}
D1_0_SDG <- makenumSDG(D1_0_SDG)We proceed by examining the missing values.
propmissing <- numeric(length(D1_0_SDG))
for (i in 1:length(D1_0_SDG)){
proportion <- mean(is.na(D1_0_SDG[[i]]))
propmissing[i] <- proportion
}
variable_names <- colnames(D1_0_SDG)
prop_missing_data <- data.frame(variable = variable_names, prop_missing = propmissing)
ggplot(prop_missing_data, aes(x = variable, y = prop_missing)) +
geom_bar(stat = "identity", fill = "skyblue", color = "black") +
labs(title = "Proportion of Missing Values by Variable",
x = "Variable",
y = "Proportion of Missing Values") +
theme_minimal()+
coord_flip()
Observing that the ‘population’ column contains numerous NAs, we investigate and discover that missing values are common, as some observations represent regions, not countries. Therefore, we can safely exclude these observations.
SDG0 <- D1_0_SDG |>
group_by(code) |>
select(population) |>
summarize(NaPop = mean(is.na(population))) |>
filter(NaPop != 0)
ggplot(SDG0, aes(x = code, y = NaPop)) +
geom_bar(stat = "identity", fill = "lightgreen", color = "black") +
labs(title = "Proportion of Missing Values in 'population' by 'code'",
x = "Code",
y = "Proportion of Missing Values") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
D1_0_SDG <- D1_0_SDG %>%
filter(!str_detect(code, "^_"))
Now, there are no missing values in the ‘population’ variable, and we observe that it contains information on 166 countries.
We notice that NAs are present in only three SDG scores: 1, 10, and 14. Additionally, when a country has NAs, they occur across all years or not at all. Consequently, we decide to conduct further investigations on these three SDG scores to determine whether to include them in our analysis.
SDG1 <- D1_0_SDG |>
group_by(code) |>
select(contains("goal")) |>
summarize(Na1 = mean(is.na(goal1)),
Na2 = mean(is.na(goal2)),
Na3 = mean(is.na(goal3)),
Na4 = mean(is.na(goal4)),
Na5 = mean(is.na(goal5)),
Na6 = mean(is.na(goal6)),
Na7 = mean(is.na(goal7)),
Na8 = mean(is.na(goal8)),
Na9 = mean(is.na(goal9)),
Na10 = mean(is.na(goal10)),
Na11 = mean(is.na(goal11)),
Na12 = mean(is.na(goal12)),
Na13 = mean(is.na(goal13)),
Na14 = mean(is.na(goal14)),
Na15 = mean(is.na(goal15)),
Na16 = mean(is.na(goal16)),
Na17 = mean(is.na(goal17))) |>
filter(Na1 != 0 | Na2 != 0 | Na3 != 0| Na4 != 0| Na5 != 0| Na6 != 0| Na7 != 0| Na8 != 0| Na9 != 0| Na10 != 0| Na11 != 0| Na12 != 0| Na13 != 0| Na14 != 0| Na15 != 0| Na16 != 0| Na17 != 0)
result_list <- list()
for (col in names(SDG1)[-1]) {
count_na <- sum(SDG1[[col]] != 0)
temp_df <- data.frame(Goal = col, Count_NA = count_na, stringsAsFactors = FALSE)
result_list <- c(result_list, list(temp_df))
}
result_df <- do.call(rbind, result_list)
ggplot(result_df, aes(x = reorder(Goal, Count_NA), y = Count_NA)) +
geom_bar(stat = "identity", fill = "lightyellow", color = "black") +
labs(title = "Count of Missing Values by Goal",
x = "Goal",
y = "Count of Missing Values") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
For goal 1, there are only 9.04% missing values in 15 different countries. Goal 1 being “End poverty”, we decide to keep it and only remove the countries with no information for the analysis.
SDG2 <- D1_0_SDG |>
group_by(code) |>
select(contains("goal")) |>
summarize(Na1 = mean(is.na(goal1))) |>
filter(Na1 != 0)
country_number <- length(unique(D1_0_SDG$country))
length(unique(SDG2$code))/country_number
#> [1] 0.0904For goal 10, there are only 10.2% missing values in 17 different countries. Goal 10 being “reduced inequalities”, we decide to keep it and only remove the countries with no information for the analysis.
SDG3 <- D1_0_SDG |>
group_by(code) |>
select(contains("goal")) |>
summarize(Na10 = mean(is.na(goal10))) |>
filter(Na10 != 0)
length(unique(SDG3$code))/country_number
#> [1] 0.102For goal 14, there are 24.1% missing values in 40 different countries. Goal 14 being “life under water”, we decide not to keep it, because other SDG such as “life on earth” and “clean water” already treat similar subjects.
SDG4 <- D1_0_SDG |>
group_by(code) |>
select(contains("goal")) |>
summarize(Na14 = mean(is.na(goal14))) |>
filter(Na14 != 0)
length(unique(SDG4$code))/country_number
#> [1] 0.241
D1_0_SDG <- D1_0_SDG %>% select(-goal14)We will work with various datasets and merge them using the country code and year as key identifiers. To ensure accurate matching, we first verify that country names are encoded in UTF-8 format. Then, we standardize the names of the countries (requiring a custom match for Turkey) and the country codes, utilizing the countrycode library. Additionally, we compile a list of all country codes from the main database to filter the other datasets. Lastly, we complete the database to include all possible “country, year” combinations, ensuring the total number of rows remains unchanged.
D1_0_SDG$country <- stri_encode(D1_0_SDG$country, to = "UTF-8")
D1_0_SDG <- D1_0_SDG %>%
mutate(country = countrycode(country, "country.name", "country.name", custom_match = c("T�rkiye"="Turkey")))
D1_0_SDG$code <- countrycode(
sourcevar = D1_0_SDG$code,
origin = "iso3c",
destination = "iso3c",
)
list_country <- c(unique(D1_0_SDG$code))
D1_0_SDG_country_list <- D1_0_SDG %>%
filter(code %in% list_country) %>%
select(code, country)
D1_0_SDG_country_list <- D1_0_SDG_country_list %>%
select(code, country) %>%
distinct()Finally, we complete the database to ensure there are no missing pairs of (year, code).
Here are the first few lines of the cleaned dataset on SDG achievement scores:
For this first dataset, we reduced the size from 4,140 observations across 120 variables to 3,818 observations for 21 variables.
As said, this is now our main dataset. All subsequent datasets will be merged with this dataset. Therefore, for all the following datasets, we will make sure that we only keep data for the same countries and years as in this dataset. We have a total of 166 countries and the years range from 2000 to 2022.
Dataset on Unemployment rate
In this dataset, the initial step involves importing the data. Next, we ensure that the names and codes of the countries are formatted in UTF-8, preventing any discrepancies due to mismatches in country names. Following this, we modify the column names and filter the data to include only the relevant countries and years, specifically the years 2000 to 2022, covering 166 countries from our primary dataset.
D2_1_Unemployment_rate <- read.csv(here("scripts","data","UnemploymentRate.csv")) %>%
as.data.frame() %>%
mutate(
country = iconv(ref_area.label, to = "UTF-8", sub = "byte"),
country = countrycode(country, "country.name", "country.name"),
year = time,
`unemployment rate` = obs_value / 100,
age_category = classif1.label,
sex = sex.label
) %>%
select(-ref_area.label, -time, -obs_value, -classif1.label, -sex.label, -source.label, -obs_status.label, -indicator.label) %>%
merge(D1_0_SDG_country_list[, c("country", "code")], by = "country", all.x = TRUE) %>%
filter(year >= 2000 & year <= 2022,
!str_detect(sex, fixed("Male")) & !str_detect(sex, fixed("Female")),
code %in% D1_0_SDG_country_list$code,
age_category == "Age (Youth, adults): 15+") %>%
select(code, country, year, `unemployment rate`) %>%
distinct()Here are the first few lines of the cleaned dataset on Unemployment rate:
For this first dataset, we reduced the size from 82,800 observations across 8 variables to 3,812 observations for 5 variables.
Dataset on GDP military Expenditures
We have three different databases which contain information on each countries over the years. Each year represent one variable. We want to extract three variables for our analysis: GDP per capita, military expenditures in percentage of the GDP and military expenditures in percentage of government expenditures.
GDPpercapita <-
read.csv(here("scripts","data","GDPpercapita.csv"), sep = ";")
MilitaryExpenditurePercentGDP <-
read.csv(here("scripts","data","MilitaryExpenditurePercentGDP.csv"), sep = ";")
MiliratyExpenditurePercentGovExp <-
read.csv(here("scripts","data","MiliratyExpenditurePercentGovExp.csv"), sep = ";")After importing the data, we fill in the missing country codes using the column Indicator.Name, because we realized after some manipulations, that some of the country codes were false, but the next column contained the right ones.
fill_code <- function(data){
data <- data %>%
mutate(Country.Code = ifelse(!grepl("^[A-Z]{3}$", Country.Code), Indicator.Name, Country.Code))
}We create a set of functions that we will apply to each database. First, remove the variables that we don’t need, which are the years before 2000. Second, make sure that the values are numeric and rename the year variables (because they all had an “X” before year number). Third, transform the database from wide to long, in order to match the main database. Fourth, transform the year variable into an integer variable and rearrange and rename the columns to match the ones of the other databases. Then, we apply these transformations to the three databases.
remove <- function(data){
years <- seq(1960, 1999)
removeyears <- paste("X", years, sep = "")
data <- data[, !(names(data) %in% c("Indicator.Name", "Indicator.Code", "X", removeyears))]
}
makenum <- function(data) {
for (i in 2000:2022) {
year <- paste("X", i, sep = "")
data[[year]] <- as.numeric(data[[year]])
}
return(data)
}
renameyear <- function(data) {
for (i in 2000:2022) {
varname <- paste("X", i, sep = "")
names(data)[names(data) == varname] <- gsub("X", "", varname)
}
return(data)
}
wide2long <- function(data) {
data <- pivot_longer(data,
cols = -c("Country.Name", "Country.Code"),
names_to = "year",
values_to = "data")
return(data)
}
yearint <- function(data) {
data$year <- as.integer(data$year)
return(data)
}
nameorder <- function(data) {
colnames(data) <- c("country", "code", "year", "data")
data <- data %>% select(c("code", "country", "year", "data"))
}
cleanwide2long <- function(data){
data <- fill_code(data)
data <- remove(data)
data <- makenum(data)
data <- renameyear(data)
data <- wide2long(data)
data <- yearint(data)
data <- nameorder(data)
}
GDPpercapita <- cleanwide2long(GDPpercapita)
MilitaryExpenditurePercentGDP <- cleanwide2long(MilitaryExpenditurePercentGDP)
MiliratyExpenditurePercentGovExp <- cleanwide2long(MiliratyExpenditurePercentGovExp)We rename the colums with the main information, standardize the country code and remove the countries that are not in our main database. We see that all the 166 countries are there.
GDPpercapita <- GDPpercapita %>%
rename(GDPpercapita = data)
MilitaryExpenditurePercentGDP <- MilitaryExpenditurePercentGDP %>%
rename(MilitaryExpenditurePercentGDP = data)
MiliratyExpenditurePercentGovExp <- MiliratyExpenditurePercentGovExp %>%
rename(MiliratyExpenditurePercentGovExp = data)
GDPpercapita$code <- countrycode(
sourcevar = GDPpercapita$code,
origin = "iso3c",
destination = "iso3c",
)
MilitaryExpenditurePercentGDP$code <- countrycode(
sourcevar = MilitaryExpenditurePercentGDP$code,
origin = "iso3c",
destination = "iso3c",
)
MiliratyExpenditurePercentGovExp$code <- countrycode(
sourcevar = MiliratyExpenditurePercentGovExp$code,
origin = "iso3c",
destination = "iso3c",
)
GDPpercapita <- GDPpercapita %>% filter(code %in% list_country)
length(unique(GDPpercapita$code))
#> [1] 166
MilitaryExpenditurePercentGDP <- MilitaryExpenditurePercentGDP %>% filter(code %in% list_country)
length(unique(MilitaryExpenditurePercentGDP$code))
#> [1] 166
MiliratyExpenditurePercentGovExp <- MiliratyExpenditurePercentGovExp %>% filter(code %in% list_country)
length(unique(MiliratyExpenditurePercentGovExp$code))
#> [1] 166There were only 157 countries that were both in the main SDG dataset and in these 3 datasets, but we suspected that some of the missing countries were in the database but not rightly matched. Indeed, Bahamas was in the database but instead of the code “BHS” there was “The”, for “COD” it was “Dem. Rep.”, for “COG” it was “Rep”, etc. We remarked that the code is in another column of the initial database: “Indicator.Name”. We went back to the initial database and before cleaning it we put the right codes (as seen above) and after rerunning the code we see that we have all our 166 countries from the initial dataset.
list_country_GDP <- c(unique(GDPpercapita$code))
(missing <- setdiff(list_country, list_country_GDP))
#> character(0)We run a first round of investigation of the missing values and find that we have 16.4% for MiliratyExpenditurePercentGovExp, 12.9% for MilitaryExpenditurePercentGDP and 1.31% for GDPpercapita.
mean(is.na(MiliratyExpenditurePercentGovExp$MiliratyExpenditurePercentGovExp))
#> [1] 0.164
mean(is.na(MilitaryExpenditurePercentGDP$MilitaryExpenditurePercentGDP))
#> [1] 0.129
mean(is.na(GDPpercapita$GDPpercapita))
#> [1] 0.0131GDP per capita
For GDPpercapita, only two countries (SOM and SSD) have a lot of missing values and in total 11 countries countries have missing values.
GDPpercapita1 <- GDPpercapita %>%
group_by(code) %>%
summarize(NaGDP = mean(is.na(GDPpercapita))) %>%
filter(NaGDP != 0)
ggplot(GDPpercapita1, aes(x = reorder(code, NaGDP), y = NaGDP, fill = code)) +
geom_bar(stat = "identity", fill="#FFEDCC", color="black") +
labs(title = "Proportion of Missing Values in 'GDPpercapita' by 'code'",
x = "Code",
y = "Proportion of Missing Values") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) 
We plot the evolution of GDPpercapita avec the years for each country containing missing values and distinguish the percentage of missing values with colors.
filtered_data_GDP <- GDPpercapita %>%
filter(code %in% GDPpercapita1$code) # countries with NAs
filtered_data_GDP <- filtered_data_GDP %>%
group_by(code) %>%
mutate(PercentageMissing = mean(is.na(GDPpercapita))) %>% # column % NAs
ungroup()
Evol_Missing_GDP <- ggplot(data = filtered_data_GDP) +
geom_point(aes(x = year, y = GDPpercapita,
color = cut(PercentageMissing,
breaks = c(0, 0.1, 0.2, 1),
labels = c("0-10%", "10-20%", "30-100%")))) +
labs(title = "Evolution of GDP per capita over time", x = "Year", y = "GDP per capita") +
scale_color_manual(values = c("0-10%" = "blue", "10-20%" = "green", "30-100%" = "black"),
labels = c("0-10%", "10-20%", "30-100%")) +
guides(color = guide_legend(title = "% missings")) +
facet_wrap(~ code, nrow = 4)
print(Evol_Missing_GDP)
For the countries with less than 30% of missing values and a linear evolution in time, we fill the missing values using linear interpolation.
list_code <- c("AFG", "BTN", "CUB", "STP", "TKM")
for (i in list_code) {
country_data <- GDPpercapita %>% filter(code == i)
interpolated_data <- na.interp(country_data$GDPpercapita)
GDPpercapita[GDPpercapita$code == i, "GDPpercapita"] <- interpolated_data
}Military expenditures in percentage of GDP
For MilitaryExpenditurePercentGDP, 12 countries have 100% of missing values. We further investigate and keep them for now, knowing that some of these coutries may also have many missing values in the other databases when wee merge everything and will be dropped later.
MilitaryExpenditurePercentGDP1 <- MilitaryExpenditurePercentGDP %>%
group_by(code) %>%
summarize(NaMil1 = round(mean(is.na(MilitaryExpenditurePercentGDP)),3)) %>%
filter(NaMil1 != 0)
ggplot(MilitaryExpenditurePercentGDP1, aes(x = reorder(code, NaMil1), y = NaMil1, fill = code)) +
geom_bar(stat = "identity", fill="#FFC0CB", color="black") +
labs(title = "Proportion of Missing Values in 'MilitaryExpenditurePercentGDP' by 'code'",
x = "Code",
y = "Proportion of Missing Values") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) 
We plot the evolution of MilitaryExpenditurePercentGDP along the years for each country containing missing values and distinguish the percentage of missing values with colors.
filtered_data_Mil1 <- MilitaryExpenditurePercentGDP %>%
filter(code %in% MilitaryExpenditurePercentGDP1$code) # countries with NAs
filtered_data_Mil1 <- filtered_data_Mil1 %>%
group_by(code) %>%
mutate(PercentageMissing = mean(is.na(MilitaryExpenditurePercentGDP))) %>% # Column % NAs
ungroup()
Evol_Missing_Mil1 <- ggplot(data = filtered_data_Mil1) +
geom_line(aes(x = year, y = MilitaryExpenditurePercentGDP,
color = cut(PercentageMissing,
breaks = c(0, 0.1, 0.2, 0.3, 1),
labels = c("0-10%", "10-20%", "20-30%", "30-100%")))) +
labs(title = "Military expenditure in % of GDP over time", x = "Years from 2000 to 2022", y = "GDP per capita") +
scale_color_manual(values = c("0-10%" = "blue", "10-20%" = "green", "20-30%" = "red", "30-100%" = "black"),
labels = c("0-10%", "10-20%", "20-30%", "50-100%")) +
guides(color = guide_legend(title = "% missings")) +
facet_wrap(~ code, nrow = 6) +
theme(strip.text = element_text(size = 6)) +
scale_x_continuous(breaks = NULL) +
scale_y_continuous(breaks = NULL)
print(Evol_Missing_Mil1)
For the countries with less than 30% of missing values and a linear evolution in time, we fill the missing values using linear interpolation.
list_code <- c("AFG", "BDI", "BEN", "CAF", "CIV", "COD", "GAB", "GMB", "KAZ", "LBN", "LBR", "MNE", "MRT", "NER", "TKJ", "TTO", "ZMB")
for (i in list_code) {
country_data <- MilitaryExpenditurePercentGDP %>% filter(code == i)
interpolated_data <- na.interp(country_data$MilitaryExpenditurePercentGDP)
MilitaryExpenditurePercentGDP[MilitaryExpenditurePercentGDP$code == i, "MilitaryExpenditurePercentGDP"] <- interpolated_data
}Military expenditures in percentage of governement expenditures
For MilitaryExpenditurePercentGovExp, 17 countries have 100% of missing values. We further investigate and keep them for now, knowing that some of these coutries may also have many missing values in the other databases when wee merge everything and will be dropped later.
MiliratyExpenditurePercentGovExp1 <- MiliratyExpenditurePercentGovExp %>%
group_by(code) %>%
summarize(NaMil2 = round(mean(is.na(MiliratyExpenditurePercentGovExp)),3)) %>%
filter(NaMil2 != 0)
ggplot(MiliratyExpenditurePercentGovExp1, aes(x = reorder(code, NaMil2), y = NaMil2, fill = code)) +
geom_bar(stat = "identity", fill="#E6E6FA", color="black") +
labs(title = "Proportion of Missing Values in 'MilitaryExpenditurePercentGDP' by 'code'",
x = "Code",
y = "Proportion of Missing Values") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 90, hjust = 1, size=8)) 
We plot the evolution of MilitaryExpenditurePercentGovExp along the years for each country containing missing values and distinguish the percentage of missing values with colors.
filtered_data_Mil2 <- MiliratyExpenditurePercentGovExp %>%
filter(code %in% MiliratyExpenditurePercentGovExp1$code) # Countries with NAs
filtered_data_Mil2 <- filtered_data_Mil2 %>%
group_by(code) %>%
mutate(PercentageMissing = mean(is.na(MiliratyExpenditurePercentGovExp))) %>% # Column % NAs
ungroup()
Evol_Missing_Mil2 <- ggplot(data = filtered_data_Mil2) +
geom_line(aes(x = year, y = MiliratyExpenditurePercentGovExp,
color = cut(PercentageMissing,
breaks = c(0, 0.1, 0.2, 0.3, 1),
labels = c("0-10%", "10-20%", "20-30%", "30-100%")))) +
labs(title = "Military expenditure in % of government expenditures over time", x = "Year from 2000 to 2022", y = "GDP per capita") +
scale_color_manual(values = c("0-10%" = "blue", "10-20%" = "green", "20-30%" = "red", "30-100%" = "black"),
labels = c("0-10%", "10-20%", "20-30%", "50-100%")) +
guides(color = guide_legend(title = "% missings")) +
facet_wrap(~ code, nrow = 7) +
theme(strip.text = element_text(size = 6)) +
scale_x_continuous(breaks = NULL) +
scale_y_continuous(breaks = NULL)
print(Evol_Missing_Mil2)
For the countries with less than 30% of missing values and a linear evolution in time, we fill the missing values using linear interpolation.
list_code <- c("AFG", "ARM", "BEN", "BIH", "BLR", "COG", "ECU", "GAB", "GMB", "KAZ", "LBN", "LBR", "MNE", "MWI", "NER", "TTO", "UKR", "ZMB")
for (i in list_code) {
country_data <- MiliratyExpenditurePercentGovExp %>% filter(code == i)
interpolated_data <- na.interp(country_data$MiliratyExpenditurePercentGovExp)
MiliratyExpenditurePercentGovExp[MiliratyExpenditurePercentGovExp$code == i, "MiliratyExpenditurePercentGovExp"] <- interpolated_data
}We now look again at the percentage of missing values for the trhee databases: 14.49% for MiliratyExpenditurePercentGovExp, 11.6% for MilitaryExpenditurePercentGDP and 1.07% for GDPpercapita
mean(is.na(MiliratyExpenditurePercentGovExp$MiliratyExpenditurePercentGovExp))
#> [1] 0.149
mean(is.na(MilitaryExpenditurePercentGDP$MilitaryExpenditurePercentGDP))
#> [1] 0.116
mean(is.na(GDPpercapita$GDPpercapita))
#> [1] 0.0107
D3_1_GDP_per_capita <- GDPpercapita
D3_2_Military_Expenditure_Percent_GDP <- MilitaryExpenditurePercentGDP
D3_3_Miliraty_Expenditure_Percent_Gov_Exp <- MiliratyExpenditurePercentGovExpHere are the first few lines of the cleaned dataset of GDP per capita:
For this dataset, we went from ??? observations for 68 variables to 3818 observations for 4 varibles.
Here are the first few lines of the cleaned dataset of military expenditures in percentage of GDP:
For this dataset, we went from ??? observations for 68 variables to 3818 observations for 4 varibles.
Here are the first few lines of the cleaned dataset of military expenditures in percentage of government expenditures:
Dataset on internet usage
To prepare the dataset on internet usage in the world to be merge with the other data, we first, import the data. Then, we keep only the year that we are interested in (2000 to 2022). We also rename the column and keep only the country that match the list of the countries in the main dataset on the SDG.
D4_0_Internet_usage <- read.csv(here("scripts", "data", "InternetUsage.csv")) %>%
filter(Year >= 2000, Year <= 2022) %>%
rename(
code = Code,
country = Entity,
year = Year,
internet_usage = Individuals.using.the.Internet....of.population.
) %>%
mutate(internet_usage = internet_usage / 100) %>%
filter(code %in% list_country) %>%
select(code, country, year, internet_usage)Here are the first few lines of the cleaned dataset of internet usage:
For this first dataset, we reduced the size from 6,570 observations across 4 variables to 3,433 observations for 4 variables.
Dataset on human freedom index
After importing the data from the CATO Institute website, we noticed that even if the file was called “Human Freedom Index 2022”, the available observations were only going from 2000 up to 2020. We have decided first to modify it in order to match our other datasets, by renaming/encoding/standardizing the columns containing the country names.
data <- read.csv(here("scripts", "data", "human-freedom-index-2022.csv"))
#data in tibble
datatibble <- tibble(data)
# Rename the column countries into country to match the other datbases
names(datatibble)[names(datatibble) == "countries"] <- "country"
# Make sure the encoding of the country names are UTF-8
datatibble$country <- iconv(datatibble$country, to = "UTF-8", sub = "byte")
# standardize country names
datatibble <- datatibble %>%
mutate(country = countrycode(country, "country.name", "country.name"))Once done, we could verify which countries were or were not present between these observations and our main SDG dataset. We have decided to keep the ones that were matching between the two datasets.
# Merge by country name
datatibble <- datatibble %>%
left_join(D1_0_SDG_country_list, by = "country")
datatibble <- datatibble %>% filter(code %in% list_country)
(length(unique(datatibble$code)))
#> [1] 159
# See which ones are missing
list_country_free <- c(unique(datatibble$code))
(missing <- setdiff(list_country, list_country_free))
#> [1] "AFG" "CUB" "MDV" "STP" "SSD" "TKM" "UZB"
# Turkey was missing but present in the initial database (it was a problem when stadardizing the country names of D1_0SDG_country_list that we corrected) and the other missing countries are:"AFG" "CUB" "MDV" "STP" "SSD" "TKM" "UZB"
D5_0_Human_freedom_index <- datatibbleThen, we noticed that there were a lot of columns that were not important for us, as we had 141 variables taken into account. So we have decided to keep the ones that refers to the countries informations (such as code, year, ..) and their human freedom scores per category (pf for personnal freedom, ef for economical freedom).
# erasing useless columns to keep only the general ones.
D5_0_Human_freedom_index <- select(D5_0_Human_freedom_index, year, country, region, hf_score, pf_rol, pf_ss, pf_movement, pf_religion, pf_assembly, pf_expression, pf_identity, pf_score, ef_government, ef_legal, ef_money, ef_trade, ef_regulation, ef_score, code)
D5_0_Human_freedom_index <- D5_0_Human_freedom_index %>%
rename(
pf_law = names(D5_0_Human_freedom_index)[5], # Renames the 5th column to "pf_law"
pf_security = names(D5_0_Human_freedom_index)[6] # Renames the 6th column to "pf_security"
)After renaming the columns pf_law/security for comprehension purpose, we have investigated how are distributed the NA values among the countries and the variables. After having found the percentages of missing values per country and variable, heatmaps revealed themself to be a great tool for visualizing datas.
na_percentage_by_country <- D5_0_Human_freedom_index %>%
group_by(country) %>%
select(-code) %>%
summarise(across(everything(), ~mean(is.na(.))*100))
na_long <- na_percentage_by_country %>%
pivot_longer(
cols = -country,
names_to = "Variable",
values_to = "NA_Percentage"
)
overall_na_percentage <- na_long %>%
group_by(Variable) %>%
summarize(Avg_NA_Percentage = mean(NA_Percentage, na.rm = TRUE)) %>%
arrange(desc(Avg_NA_Percentage))
print(overall_na_percentage)
#> # A tibble: 17 x 2
#> Variable Avg_NA_Percentage
#> <chr> <dbl>
#> 1 ef_money 10.4
#> 2 ef_trade 10.4
#> 3 ef_score 10.4
#> 4 hf_score 10.4
#> 5 pf_score 10.4
#> 6 ef_regulation 9.49
#> 7 ef_government 2.91
#> 8 ef_legal 1.71
#> 9 pf_law 1.44
#> 10 pf_identity 0.299
#> 11 pf_assembly 0
#> 12 pf_expression 0
#> 13 pf_movement 0
#> 14 pf_religion 0
#> 15 pf_security 0
#> 16 region 0
#> 17 year 0Then, for having a better understanding of the situation, we ordered the countries having at least 1 variable containing 50% and more of missing values
na_long <- na_long %>%
group_by(country) %>%
mutate(Count_NA_50_100 = sum(NA_Percentage >= 50 & NA_Percentage <= 100, na.rm = TRUE)) %>%
ungroup() %>%
arrange(desc(Count_NA_50_100))
heatmap_ordered_all <- ggplot(na_long, aes(x = reorder(country, -Count_NA_50_100), y = Variable)) +
geom_tile(aes(fill = NA_Percentage), colour = "white") +
scale_fill_gradient(low = "white", high = "red") +
theme_minimal() +
labs(
title = "Heatmap of NA Percentages per Country and Variable",
x = "Countries",
y = "Variables",
fill = "NA Percentage"
) +
theme(
axis.text.x = element_blank(), # Hide x-axis labels
axis.text.y = element_text(size = 9)
)
print(heatmap_ordered_all)
We notice that only some countries look to contain at least 50% of missing values and in addition that most of the missing values are concerning the EF variables (Economic Freedom). Now, we tried to produce another heatmap only containing the ordered countries, and also counting for each one of these country the number of variables with at least 50% of NAs.
na_long_filtered <- na_long %>%
group_by(country) %>%
mutate(Count_NA_50_100 = sum(NA_Percentage >= 50 & NA_Percentage <= 100, na.rm = TRUE)) %>%
filter(Count_NA_50_100 > 0) %>%
ungroup() %>%
arrange(desc(Count_NA_50_100))
heatmap_ordered_filtered <- ggplot(na_long_filtered, aes(x = reorder(country, -Count_NA_50_100), y = Variable)) +
geom_tile(aes(fill = NA_Percentage), colour = "white") +
scale_fill_gradient(low = "white", high = "red") +
theme_minimal() +
labs(
title = "Heatmap of NA Percentages per Country and Variable",
x = "Countries",
y = "Variables",
fill = "NA Percentage"
) +
theme(
axis.text.x = element_text(angle = 90, hjust = 1),
axis.text.y = element_text(size = 7)
)
print(heatmap_ordered_filtered)
country_na_count <- na_long %>%
filter(NA_Percentage >= 50) %>%
group_by(country) %>%
summarise(Count_NA_50_100 = n()) %>%
arrange(desc(Count_NA_50_100))
print(country_na_count)
#> # A tibble: 13 x 2
#> country Count_NA_50_100
#> <chr> <int>
#> 1 Comoros 8
#> 2 Djibouti 8
#> 3 Somalia 8
#> 4 Belarus 6
#> 5 Guinea 6
#> 6 Iraq 6
#> 7 Laos 6
#> 8 Sudan 6
#> 9 Bhutan 5
#> 10 Liberia 5
#> 11 Bahamas 1
#> 12 Belize 1
#> 13 Brunei 1
We conclude here that 13 countries were concerned by our selection of 50% and more of missing values. When discussing between us, we came to the conclusion that among these 13 countries, a great part of them were not going to be selected because they had a lot of missing values in our main dataset too. Therefore, we have decided to merge this data with the other datasets and finish the cleaning after.
Here are the first few lines of the partialy cleaned dataset on Human Freedom Index scores:
For this first dataset, we reduced the size from 3’465 observations across 141 variables to 3339 observations for 4 variables.
Dataset on Disasters
For this dataset concerning the Disasters we imported the data from Kaggle as we couldn’t find the original dataset that is private coming from the EOSDIS SYSTEM, an interactive interface for browsing full-resolution, global, daily satellite images from NASA. Once we made sure that our file called “Disasters” was convert into a data frame, we selected some specific columns that we where interested in.
Disasters <- as.data.frame(read.csv(here("scripts", "data", "Disasters.csv"))) %>%
select(Year, Country, ISO, Location, Continent, Disaster.Subgroup, Disaster.Type, Total.Deaths, No.Injured, No.Affected, No.Homeless, Total.Affected, Total.Damages...000.US..)Because we knew that our file showed all the disasters in each country over the years (between 1970-2021) and that we wanted to focus on a specific period, we filtered our data to show the years between 2000 and 2022. Then we rearranged our data, changing the data types of all the columns and their names in order to match our other datasets.
# Rearrange the columns, changed the type of data, renamed the columns
Rearanged_Disasters <- Disasters %>%
filter(Year >= 2000 & Year <= 2022) %>%
mutate(
code = as.character(ISO),
country = as.character(Country),
year = as.integer(Year),
continent = as.character(Continent),
disaster.subgroup = as.character(Disaster.Subgroup),
disaster.type = as.character(Disaster.Type),
location = as.character(Location),
total.deaths = as.numeric(Total.Deaths),
no.injured = as.numeric(No.Injured),
no.affected = as.numeric(No.Affected),
no.homeless = as.numeric(No.Homeless),
total.affected = as.numeric(Total.Affected),
total.damages = as.numeric(Total.Damages...000.US..)
)We then grouped the data by “year”, “code”, “country” and “continent” and summarize the data. Here you can see that we re-selected specific columns as we saw that our first pre-selection was still too wide and some variables as the disaster.subgroup and disaster.type weren’t pertinent.We arranged the columns based on “code,” “country,” “year,” and “continent” to match the other datasets.
Disasters <- Rearanged_Disasters %>%
group_by(year,code, country, continent) %>%
summarize(
total_deaths = sum(total.deaths, na.rm = TRUE),
no_injured = sum(no.injured, na.rm = TRUE),
no_affected = sum(no.affected, na.rm = TRUE),
no_homeless = sum(no.homeless, na.rm = TRUE),
total_affected = sum(total.affected, na.rm = TRUE),
total_damages = sum(total.damages, na.rm = TRUE)
)
D6_0_Disasters <- Disasters %>%
select(code, country, year, continent, total_deaths, no_injured, no_affected, no_homeless, total_affected, total_damages) %>%
arrange(code, country, year, continent)Finally we filtered our disasters data to keep only the countries that are present in our main dataset. We analysed the missing countries and identified three countries (BHR, BRN, MLT) that are unexpectedly missing.
D6_0_Disasters <- D6_0_Disasters %>% filter(code %in% list_country)
length(unique(D6_0_Disasters$code))
#> [1] 163
# Here we see which countries are missing
list_country_disasters <- c(unique(D6_0_Disasters$code))
(missing <- c(missing,setdiff(list_country, list_country_disasters)))
#> [1] "AFG" "CUB" "MDV" "STP" "SSD" "TKM" "UZB" "BHR" "BRN" "MLT"Here are the first few lines of the cleaned dataset on Disasters:
Dataset on COVID
This dataset contains information on the COVID19 pandemic between 2020 and 2022. The observation are by year, month, day. After importing the database, we transform the date in format YYYY-MM-DD in order to only keep the year.
COVID <- read.csv(here("scripts","data","COVID.csv"))
COVID <- COVID[,c("iso_code", "location", "date", "new_cases_per_million", "new_deaths_per_million", "stringency_index")]
COVID$date <- as.integer(year(COVID$date))We perform a first round of investigation of the missing values before aggregating the values by year. We begin with the variables “cases per million” and “deaths per million”: seeing that for each country, we have either only missing values, either a very low percentage of missing values (~1%), we can compute the sum over each year and ignore the missing values without altering the data. Indeed, where all the values are missing, the computation will return a NA. We then look at the “stringency” variable and we have 3 scenarios:
~20% missings: we ignore missing values when computing the mean to have an idea of stringency each year (because we compute the mean stringency over the year, if some days are missing, it is not a problem, it can not evoluate that fast).
all are missing: we can ignore the missing values when computing the mean, because it will still return a missing value
almost all are missing: here the mean doesn’t make sense -> we will replace the values by NAs to be coherent. The countries with this issues are: ERI, GUM, PRI and VIR. We verify if they are in our main dataset and since none of these countries are, we can ignore the issue, the lines will be remove later anyway.
We aggregate the observations of all days of a year in one observation per country using the mean.
COVID1 <- COVID %>%
group_by(iso_code) %>%
summarize(NaDeaths = round(mean(is.na(new_deaths_per_million)),3),
NaCases = round(mean(is.na(new_cases_per_million)), 3),
NaStringency = round(mean(is.na(stringency_index)), 3)) %>%
pivot_longer(cols = starts_with("Na"), names_to = "Variable", values_to = "NaValue")%>%
filter(NaValue!=0)
issue_list <- c("ERI", "GUM", "PRI", "VIR")
is.element(issue_list, list_country)
#> [1] FALSE FALSE FALSE FALSE
COVID <- COVID %>%
group_by(location, date) %>%
mutate(
cases_per_million = sum(new_cases_per_million, na.rm = TRUE),
deaths_per_million = sum(new_deaths_per_million, na.rm = TRUE),
stringency = mean(stringency_index, na.rm = TRUE)
)%>%
ungroup()
###
# Create a bubble plot
plot_ly(COVID1, x=~Variable, y=~NaValue,
type = "scatter",
marker = list(color="blue", opacity=0.1, size = 10))Now that all the variables of interest are aggregated by year, we remove all the variables that we don’t need and rename all the remaining variables to match the main dataset.
COVID <- COVID %>%
group_by(location, date) %>%
distinct(date, .keep_all = TRUE) %>%
ungroup()
COVID <- COVID %>% select(-c(new_cases_per_million, new_deaths_per_million, stringency_index))
colnames(COVID) <- c("code", "country", "year", "cases_per_million", "deaths_per_million", "stringency")We remove the years that exceed 2022, we make sure that the country codes are all iso codes with 3 letters (we observe that sometimes they are preceded by “OWID_”) and we standardize the country codes.
COVID <- COVID[COVID$year <= 2022, ]
COVID$code <- gsub("OWID_", "", COVID$code)
COVID$code <- countrycode(
sourcevar = COVID$code,
origin = "iso3c",
destination = "iso3c"
)We remove the observations of countries that aren’t in our main dataset on SDGs and find that all the 166 countries that we have in the main SDG dataset are also in this one.
COVID <- COVID %>% filter(code %in% list_country)
length(unique(COVID$code))
#> [1] 166We perform a second round of missing values investigation and find out that there are no missing values except for the stringency, where there are 4.19%. Either all values are missing for one country, or 50% are missing, so these 7 countries won’t be included when analyzing the effect of stringency on the SDG scores.
mean(is.na(COVID$cases_per_million))
#> [1] 0
mean(is.na(COVID$deaths_per_million))
#> [1] 0
mean(is.na(COVID$stringency))
#> [1] 0.0419
COVID4 <- COVID %>%
group_by(code) %>%
summarize(NaCOVID = mean(is.na(stringency))) %>%
filter(NaCOVID != 0)
ggplot(COVID4, aes(x = reorder(code, NaCOVID), y = NaCOVID)) +
geom_bar(stat = "identity", fill = "lightgreen", color = "black") +
labs(title = "Proportion of Missing Values in 'stringency' by 'code'",
x = "Code",
y = "Proportion of Missing Values") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
D7_0_COVID <- COVID
Here are the first few lines of the cleaned dataset on COVID19:
Dataset on Conflicts
For our conflicts dataset, we imported the data from “The World Banck” data catalog. Once we made sure that our file called “Disasters” was convert into a data frame, we selected some specific columns that we where interested in.
Conflicts <- read.csv(here("scripts","data","Conflicts.csv"))
Conflicts <- as.data.frame(Conflicts)
Conflicts <- Conflicts %>%
select(year, country, ongoing, gwsum_bestdeaths, pop_affected, peaceyearshigh, area_affected, maxintensity, maxcumulativeintensity)Our file showed all the Conflicts and consequences per country over the years (between 2000-2016). We couldn’t find a better and more complete dataset, As we consider conflicts as events, we will only take into account results between 2000 and 2016. Then we rearranged our data, changing the data types of all the columns and their names in order to match our other datasets. We grouped the data by ” year”, “country”, re-selected some variables and summarize the data.
Rearanged_Conflicts <- Conflicts %>%
filter(year >= 2000 & year <= 2022)%>%
mutate(
ongoing = as.integer(ongoing),
country = as.character(country),
year = as.integer(year),
gwsum_bestdeaths = as.numeric(gwsum_bestdeaths),
pop_affected = as.numeric(pop_affected),
area_affected = as.numeric(area_affected),
maxintensity = as.numeric(maxintensity),
)
# Group the data by "year", "country" and summarize the data
Conflicts <- Rearanged_Conflicts %>%
group_by(year, country) %>%
summarize(
ongoing = sum (ongoing, na.rm = TRUE),
sum_deaths = sum(gwsum_bestdeaths, na.rm = TRUE),
pop_affected = sum(pop_affected, na.rm = TRUE),
area_affected = sum(area_affected, na.rm = TRUE),
maxintensity = sum(maxintensity, na.rm = TRUE),
)After we Selected specific columns from the summarized data and arrange the data by our specified columns. To make our dataset compatible with the main one and let the merging face succeed, we dd some adjustment concerning the country names’ to ensure the compatibility. Then we standardize and merge by country names to finally rearrange the data to retain only the countries present in our main dataset. Note that in the end we can see that only one country is missing that wasn’t in the initial conflicts database: BLR
conflicts <- Conflicts %>%
select(country, year, ongoing, sum_deaths, pop_affected, area_affected, maxintensity) %>%
arrange(country, year)
conflicts$country <- iconv(conflicts$country, to = "UTF-8", sub = "byte")
conflicts <- conflicts %>%
mutate(country = countrycode(country, "country.name", "country.name"))
conflicts <- conflicts %>%
left_join(D1_0_SDG_country_list, by = "country")
conflicts <- conflicts %>%
select(code, country, year, ongoing, sum_deaths, pop_affected, area_affected, maxintensity) %>%
arrange(code, country, year)
D8_0_Conflicts <- conflicts %>% filter(code %in% list_country)
(length(unique(conflicts$code)))
#> [1] 166
# See which countries are missing
list_country_conflicts <- c(unique(conflicts$code))
(missing <- c(missing, setdiff(list_country, list_country_conflicts)))
#> [1] "AFG" "CUB" "MDV" "STP" "SSD" "TKM" "UZB" "BHR" "BRN" "MLT"
#> [11] "BLR"Here are the first few lines of the cleaned dataset on Conflicts:
Merge data
By merging our eight pre-cleaned datasets, we create a final database.
D2_1_Unemployment_rate$country <- NULL
merge_1_2 <- D1_0_SDG |> left_join(D2_1_Unemployment_rate, join_by(code, year))
D3_1_GDP_per_capita$country <- NULL
merge_12_3 <- merge_1_2 |> left_join(D3_1_GDP_per_capita, join_by(code, year))
D3_2_Military_Expenditure_Percent_GDP$country <- NULL
merge_12_3 <- merge_12_3 |> left_join(D3_2_Military_Expenditure_Percent_GDP, join_by(code, year))
D3_3_Miliraty_Expenditure_Percent_Gov_Exp$country <- NULL
merge_12_3 <- merge_12_3 |> left_join(D3_3_Miliraty_Expenditure_Percent_Gov_Exp, join_by(code, year))
D4_0_Internet_usage$country <- NULL
merge_123_4 <- merge_12_3 |> left_join(D4_0_Internet_usage, join_by(code, year))
D5_0_Human_freedom_index$country <- NULL
merge_1234_5 <- merge_123_4 |> left_join(D5_0_Human_freedom_index, join_by(code, year))
D6_0_Disasters$country <- NULL
merge_12345_6 <- merge_1234_5 |> left_join(D6_0_Disasters, join_by(code, year))
D7_0_COVID$country <- NULL
D7_0_COVID <- D7_0_COVID |> distinct(code, year, .keep_all = TRUE)
merge_123456_7 <- merge_12345_6 |> left_join(D7_0_COVID, join_by(code, year))
D8_0_Conflicts$country <- NULL
all_Merge <- merge_123456_7 |> left_join(D8_0_Conflicts, join_by(code, year))
all_Merge <- all_Merge %>% filter(!code %in% missing)Cleaning of the final database
We replace the NAs of the COVID columns by 0 (because we don’t have real missing, only introduced by merging for the years before COVID).
all_Merge <- all_Merge %>%
mutate(
cases_per_million = ifelse(is.na(cases_per_million), 0, cases_per_million),
deaths_per_million = ifelse(is.na(deaths_per_million), 0, deaths_per_million),
stringency = ifelse(is.na(stringency), 0, stringency)
)Since we took the information on the continent and region from databases that are not the main one, we complete these inforamtion for the whole final dataset.
all_Merge <- all_Merge %>%
group_by(country) %>%
mutate(continent = ifelse(is.na(continent), first(na.omit(continent)), continent)) %>%
ungroup()
all_Merge <- all_Merge %>%
group_by(country) %>%
mutate(region = ifelse(is.na(region), first(na.omit(region)), region)) %>%
ungroup()We order the database, beginning by the information on the country, the year, the continent and the region.
all_Merge <- all_Merge %>%
select(code, year, country, continent, region, everything())
write.csv(all_Merge, file = here("scripts","data","all_Merge.csv"))Here are the first few lines of the final dataset:
Final structure of our merged database: each country of the 166 countries from D1_1_SDG are observed each year from 2000 to 2022, thus each row has a key composed of (code, year) that uniquely identifies an observation. The other columns are the variables listed above. Due to some countries having a lot of missing information we will have to eliminate some of them, but we will still have more than 2000 rows in our database.
Treatment of missing values
We load our final database and we vizualize the missing values.
all_Merge <- read.csv(here("scripts","data","all_Merge.csv"))
all_Merge <- all_Merge %>% select(-c(X))
# Create a dataframe with the goals without NAs summarize in one column to simplify the visualization
goal_vars <- all_Merge %>%
select(starts_with("goal")) %>%
filter_all(all_vars(!is.na(.))) %>%
colnames()
to_plot_missing <- all_Merge %>%
mutate(Goals_without_NAs = rowSums(!is.na(select(., all_of(goal_vars))))) %>%
select(-c(goal2, goal3, goal4, goal5, goal6, goal7, goal8, goal9, goal11, goal12, goal13, goal15, goal16, goal17))
vis_dat(to_plot_missing, warn_large_data = FALSE) + scale_fill_brewer(palette = "Paired") +
theme(
axis.text.x = element_text(angle = 90, size = 6),
legend.text = element_text(size = 8), # Adjust the size of legend text
legend.title = element_text(size = 10)
)
We subset our database according to the data that we will need in order to answer the different questions. This will help us dealing with the missing values.
For question 1, we only keep the years until 2020, because most of the explanatory variables that we want to use (those coming from the human freedom index) only have values until 2020.
data_question1 <- all_Merge %>% filter(year<=2020) %>% select(-c(total_deaths, no_injured, no_affected, no_homeless, total_affected, total_damages, cases_per_million, deaths_per_million, stringency, ongoing, sum_deaths, pop_affected, area_affected, maxintensity))For question 2 and 4, we use the main data from the SDG database.
data_question24 <- all_Merge %>% select(c(code, year, country, continent, region, overallscore, goal1, goal2, goal3, goal4, goal5, goal6, goal7, goal8, goal9, goal10, goal11, goal12, goal13, goal15, goal16, goal17))For question 3, we create 3 distinct databases according to the different type of event that we wwill analyse: disasters, COVID19 and conflicts. For the disasters, we only keep the years until 2021, because after this date, we don’t have data. For the conflicts, we only keep the years until 2016, because after this date, we don’t have data.
# Disasters
data_question3_1 <- all_Merge %>% filter(year<=2021) %>% select(c(code, year, country, continent, region, overallscore, goal1, goal2, goal3, goal4, goal5, goal6, goal7, goal8, goal9, goal10, goal11, goal12, goal13, goal15, goal16, goal7, total_deaths, no_injured, no_affected, no_homeless, total_affected, total_damages))
# COVID
data_question3_2 <- all_Merge %>% select(c(code, year, country, continent, region, overallscore, goal1, goal2, goal3, goal4, goal5, goal6, goal7, goal8, goal9, goal10, goal11, goal12, goal13, goal15, goal16, goal7, cases_per_million, deaths_per_million, stringency))
# Conflicts
data_question3_3 <- all_Merge %>% filter(year<=2016) %>% select(c(code, year, country, continent, region, overallscore, goal1, goal2, goal3, goal4, goal5, goal6, goal7, goal8, goal9, goal10, goal11, goal12, goal13, goal15, goal16, goal7, ongoing, sum_deaths, pop_affected, area_affected, maxintensity))Data for question 1
We begin by visualizing the missing values. To have a less messy graph we group all the goals wihtout NAs into one single variable.
# Create a dataframe with the goals without NAs summarize in one column to simplify the visualization
variable_names <- names(data_question1)
missing_percentages <- sapply(data_question1, function(col) mean(is.na(col)) * 100)
missing_data_summary <- data.frame(
Variable = variable_names,
Missing_Percentage = missing_percentages
)
missing_data_summary <- missing_data_summary %>%
mutate(VariableGroup = ifelse(startsWith(Variable, "goal") & Missing_Percentage == 0, "Goals without NAs", as.character(Variable)))
ggplot(data = missing_data_summary, aes(x = reorder(VariableGroup, Missing_Percentage), y = Missing_Percentage, fill = Missing_Percentage)) +
geom_bar(stat = "identity") +
geom_text(aes(label = ifelse(Missing_Percentage > 1, sprintf("%.1f%%", Missing_Percentage), ""),
y = Missing_Percentage),
position = position_stack(vjust = 1), # Adjust vertical position
color = "white", # Text color
size = 2, # Text size
hjust = 1.05) +
labs(title = "Percentage of Missing Values by Variable",
x = "Variable",
y = "Missing Percentage") +
theme_minimal() +
theme(axis.text.y = element_text(hjust = 1, size=6 ),
legend.text = element_text(size = 8),
legend.title = element_text(size = 10)) +
labs(fill = "% NAs") +
coord_flip()
We create a column with the number of missing values by country over all the variables, except goal 1 and goal 10 that we already discussed. We decide to remove the countries that have more than 50 missing values.
see_missing1_1 <- data_question1 %>%
group_by(code) %>%
summarise(across(-c(year, country, continent, region, population, overallscore, goal1, goal2, goal3, goal4, goal5, goal6, goal7, goal8, goal9, goal10, goal11, goal12, goal13, goal15, goal16, goal17),
~ sum(is.na(.))) %>%
mutate(num_missing = rowSums(across(everything()))) %>%
filter(num_missing > 50))
data_question1 <- data_question1 %>% filter(!code %in% see_missing1_1$code)
list_country_deleted <- c(unique(see_missing1_1$code))Here is the graph that allows us to visualize the countries that have missing values, how many and for which variables, when there are more than 50 NAs in total.
ggplot(see_missing1_1, aes(x = num_missing , y = reorder(code, num_missing), fill = num_missing)) +
geom_bar(stat = "identity") +
scale_fill_gradient(low = "lightgreen", high = "darkgreen") +
theme_minimal() +
theme(axis.text.y = element_text(hjust = 1, size=8 ),
legend.text = element_text(size = 8),
legend.title = element_text(size = 10)) +
labs(title = "Number of missing values per country containing at least 50 NAs", x = "Number of Missing Values", y = "Countries")
Now, looking at the remaining countries that have missing values and there number accross all variables, we decide to remove MilitaryExpenditurePercentGovExp, because it has too many missing values and it contains similar information to MilitaryExpenditurePercentGDP.
see_missing1_2 <- data_question1 %>%
group_by(code) %>%
summarise(across(-c(year, country, continent, region, population, overallscore, goal1, goal2, goal3, goal4, goal5, goal6, goal7, goal8, goal9, goal10, goal11, goal12, goal13, goal15, goal16, goal17),
~ sum(is.na(.))) %>%
mutate(num_missing = rowSums(across(everything()))) %>%
filter(num_missing > 0))
data_question1 <- data_question1 %>% select(-MiliratyExpenditurePercentGovExp)Here is the ggplot that helps us to visualize the countries that have missing values after removing the countries with more than 50 NAs.
ggplot(see_missing1_2, aes(x = num_missing , y = reorder(code, num_missing), fill = num_missing)) +
geom_bar(stat = "identity", width = 0.5) +
scale_fill_gradient(low = "lightgreen", high = "darkgreen") +
theme_minimal() +
theme(axis.text.y = element_text(hjust = 1, size= 6 ),
legend.text = element_text(size = 8),
legend.title = element_text(size = 10)) +
labs(title = "Number of missing values per country", x = "Number of Missing Values", y = "Countries")
GDP per capita
Only Venezuela has missing values that we can not fill, so we delete the country.
question1_missing_GDP <- data_question1 %>%
group_by(code) %>%
summarize(NaGDPpercapita = mean(is.na(GDPpercapita)))%>%
filter(NaGDPpercapita != 0)
data_question1 <- data_question1 %>% filter(code!="VEN")
list_country_deleted <- c(list_country_deleted, "VEN")Military expenditure in % of GDP
To begin with, we delete the countries with more than 30% missing values.
question1_missing_Military <- data_question1 %>%
group_by(code) %>%
summarize(NaMilitary = mean(is.na(MilitaryExpenditurePercentGDP)))%>%
filter(NaMilitary != 0)
data_question1 <- data_question1 %>% filter(code!="BRB" & code!="CRI" & code!="HTI" & code!="ISL" & code!="PAN" & code!="SYR")
list_country_deleted <- c(list_country_deleted, "BRB", "CRI", "HTI", "ISL", "PAN", "SYR") Then, we look at the distribution of the variable per region. Seeing that all are skewed distributions, we decide to replace the missing values, where there are less than 30% missing using the median by region.
question1_missing_Military <- data_question1 %>%
group_by(code) %>%
mutate(PercentageMissing = mean(is.na(MilitaryExpenditurePercentGDP))) %>% # Column % NAs
ungroup() %>%
group_by(region) %>%
filter(sum(PercentageMissing, na.rm = TRUE) > 0)
Freq_Missing_Military <- ggplot(data = question1_missing_Military) +
geom_histogram(aes(x = MilitaryExpenditurePercentGDP,
fill = cut(PercentageMissing,
breaks = c(0, 0.1, 0.2, 0.3, 1),
labels = c("0-10%", "10-20%", "20-30%", "30-100%"))),
bins = 30) +
labs(title = "Distribution of Military expenditures in % of GDP", x = "Military expenditures in % of GDP", y = "Frequency") +
scale_fill_manual(values = c("0-10%" = "blue", "10-20%" = "green", "20-30%"="red","30-100%" = "black"), labels = c("0-10%", "10-20%", "20-30%","30-100%")) +
guides(fill = guide_legend(title = "% missings")) +
facet_wrap(~ region, nrow = 3)
print(Freq_Missing_Military)
data_question1 <- data_question1 %>%
group_by(code) %>%
mutate(
PercentageMissingByCode = mean(is.na(MilitaryExpenditurePercentGDP))
) %>%
ungroup() %>%
group_by(region) %>%
mutate(
MedianByRegion = median(MilitaryExpenditurePercentGDP, na.rm = TRUE),
MilitaryExpenditurePercentGDP = ifelse(
PercentageMissingByCode < 0.3 & !is.na(MilitaryExpenditurePercentGDP),
MilitaryExpenditurePercentGDP,
ifelse(PercentageMissingByCode < 0.3, MedianByRegion, MilitaryExpenditurePercentGDP)
)
) %>%
select(-PercentageMissingByCode, -MedianByRegion)
Internet usage
There are only low percentage of missing values.
question1_missing_Internet <- data_question1 %>%
group_by(code) %>%
summarize(NaInternet = mean(is.na(internet_usage)))%>%
filter(NaInternet != 0)We look at the evolution of the variable over time. We fill the missing values with linear interpolation, because all evolutions are in an increasing way and are almost straight lines, except for CIV that we delete.
question1_missing_Internet <- data_question1 %>%
group_by(code) %>%
mutate(PercentageMissing = mean(is.na(internet_usage))) %>% # Column % NAs
filter(code %in% question1_missing_Internet$code)
Evol_Missing_Internet <- ggplot(data = question1_missing_Internet) +
geom_line(aes(x = year, y = internet_usage,
color = cut(PercentageMissing,
breaks = c(0, 0.1, 0.2, 0.3, 1),
labels = c("0-10%", "10-20%", "20-30%", "30-100%")))) +
labs(title = "Evolution of internet usage over time", x = "Years from 2000 to 2022", y = "Internet usage") +
scale_color_manual(values = c("0-10%" = "blue", "10-20%" = "green", "20-30%" = "red", "30-100%" = "black"),
labels = c("0-10%", "10-20%", "20-30%", "50-100%")) +
guides(color = guide_legend(title = "% missings")) +
scale_x_continuous(breaks=NULL)+
facet_wrap(~ code, nrow = 4)
print(Evol_Missing_Internet)
list_code <- setdiff(unique(question1_missing_Internet$code), "CIV")
for (i in list_code) {
country_data <- data_question1 %>% filter(code == i)
interpolated_data <- na.interp(country_data$internet_usage)
data_question1[data_question1$code == i, "internet_usage"] <- interpolated_data
}
data_question1 <- data_question1 %>% filter(code!="CIV")
list_country_deleted <- c(list_country_deleted, "CIV") 
Human freedom index
First, we remove hf_score, pf_score and ef_score, because there are many missing values and since these variables summarize the other ones, deleting the will not make us loose information.
data_question1 <- data_question1 %>% select(-c(hf_score, pf_score, ef_score))Personal freedom: law
The variable pf_law has (many) NAs, but only for one country: BLZ, so we decide to remove it.
data_question1 <- data_question1 %>% filter(code!="BLZ")
list_country_deleted <- c(list_country_deleted, "BLZ") Economic freedom: government
Only KGZ and SRB have missing values, we plot the values over time and fill in the missing values by the year before, since there are only one and two missing values.
data_question1 %>%
filter(code %in% c("KGZ", "SRB")) %>%
ggplot(aes(x = year, y = ef_government)) +
geom_point(color = "green") +
facet_wrap(~ code, nrow = 1) +
labs(title = "Evolution of economic freedom: government over time", x = "Years", y = "ef_gov")
data_question1 <- data_question1 %>%
mutate(ef_government = ifelse(code == "KGZ" & year == 2000 & is.na(ef_government), ef_government[which(code == "KGZ" & year == 2001)], ef_government))
data_question1 <- data_question1 %>%
mutate(ef_government = ifelse(code == "SRB" & year == 2000 & is.na(ef_government), ef_government[which(code == "SRB" & year == 2002)], ef_government))
data_question1 <- data_question1 %>%
mutate(ef_government = ifelse(code == "SRB" & year == 2001 & is.na(ef_government), ef_government[which(code == "SRB" & year == 2002)], ef_government))
Economic freedom: money
18 countries have missing values, but the percentage of missing values is always below 25%.
question1_missing_ef_money <- data_question1 %>%
group_by(code) %>%
summarize(Na_ef_money = mean(is.na(ef_money)))%>%
filter(Na_ef_money != 0)We look at the evolution of the variable over time. For the countries where this evolution is linear, we fill in the missing values using linear interpolation.
question1_missing_ef_money <- data_question1 %>%
group_by(code) %>%
mutate(PercentageMissing = mean(is.na(ef_money))) %>% # Column % NAs
filter(code %in% question1_missing_ef_money$code)
Evol_Missing_ef_money <- ggplot(data = question1_missing_ef_money) +
geom_line(aes(x = year, y = ef_money,
color = cut(PercentageMissing,
breaks = c(0, 0.1, 0.2, 0.3, 1),
labels = c("0-10%", "10-20%", "20-30%", "30-100%")))) +
labs(title = "Evolution of economic freedom: money over time", x = "Years from 2000 to 2022", y = "ef_money") +
scale_color_manual(values = c("0-10%" = "blue", "10-20%" = "green", "20-30%" = "red", "30-100%" = "black"),
labels = c("0-10%", "10-20%", "20-30%", "50-100%")) +
guides(color = guide_legend(title = "% missings")) +
facet_wrap(~ code, nrow = 4) +
scale_x_continuous(breaks = NULL)
print(Evol_Missing_ef_money)
list_code <- c("ARM", "BFA", "BIH", "GEO", "KAZ", "LSO", "MDA", "MKD")
for (i in list_code) {
country_data <- data_question1 %>% filter(code == i)
interpolated_data <- na.interp(country_data$ef_money)
data_question1[data_question1$code == i, "ef_money"] <- interpolated_data
}
Then, we look at the distribution of the variable per region. Seeing that all are skewed distributions, we decide to replace the missing values using the median by region.
question1_missing_ef_money <- data_question1 %>%
group_by(code) %>%
mutate(PercentageMissing = mean(is.na(ef_money))) %>% # Column % NAs
ungroup() %>%
group_by(region) %>%
filter(sum(PercentageMissing, na.rm = TRUE) > 0)
Freq_Missing_ef_money <- ggplot(data = question1_missing_ef_money) +
geom_histogram(aes(x = ef_money,
fill = cut(PercentageMissing,
breaks = c(0, 0.1, 0.2, 0.3, 1),
labels = c("0-10%", "10-20%", "20-30%", "30-100%"))),
bins = 30) +
labs(title = "Distribution of economic freedom: money", x = "ef_money", y = "Frequency") +
scale_fill_manual(values = c("0-10%" = "blue", "10-20%" = "green", "20-30%"="red","30-100%" = "black"), labels = c("0-10%", "10-20%", "20-30%","30-100%")) +
guides(fill = guide_legend(title = "% missings")) +
facet_wrap(~ region, nrow = 2)
print(Freq_Missing_ef_money)
data_question1 <- data_question1 %>%
group_by(code) %>%
mutate(
PercentageMissingByCode = mean(is.na(ef_money))
) %>%
ungroup() %>%
group_by(region) %>%
mutate(
MedianByRegion = median(ef_money, na.rm = TRUE),
ef_money = ifelse(
PercentageMissingByCode < 0.3 & !is.na(ef_money),
ef_money,
ifelse(PercentageMissingByCode < 0.3, MedianByRegion, ef_money)
)
) %>%
select(-PercentageMissingByCode, -MedianByRegion)
Economic freedom: trade
19 countries have missing values, but the percentage of missing values is always below 25%.
question1_missing_ef_trade <- data_question1 %>%
group_by(code) %>%
summarize(Na_ef_trade = mean(is.na(ef_trade)))%>% # Column % NAs
filter(Na_ef_trade != 0)
question1_missing_ef_trade <- data_question1 %>%
group_by(code) %>%
mutate(PercentageMissing = mean(is.na(ef_trade))) %>%
filter(code %in% question1_missing_ef_trade$code)We look at the evolution of the variable over time. For the countries where this evolution is linear, we fill in the missing values using linear interpolation.
Evol_Missing_ef_trade <- ggplot(data = question1_missing_ef_trade) +
geom_line(aes(x = year, y = ef_trade,
color = cut(PercentageMissing,
breaks = c(0, 0.1, 0.2, 0.3, 1),
labels = c("0-10%", "10-20%", "20-30%", "30-100%")))) +
labs(title = "Evolution of economic freedom: trade over time", x = "Years from 2000 to 2022", y = "ef_trade") +
scale_color_manual(values = c("0-10%" = "blue", "10-20%" = "green", "20-30%" = "red", "30-100%" = "black"),
labels = c("0-10%", "10-20%", "20-30%", "50-100%")) +
guides(color = guide_legend(title = "% missings")) +
facet_wrap(~ code, nrow = 4) +
scale_x_continuous(breaks = NULL)
print(Evol_Missing_ef_trade)
# Linear interpolation for "AZE", "BFA", "ETH", "GEO", "VNH"
list_code <- c("AZE", "BFA", "ETH", "GEO", "VNH")
for (i in list_code) {
country_data <- data_question1 %>% filter(code == i)
interpolated_data <- na.interp(country_data$ef_trade)
data_question1[data_question1$code == i, "ef_trade"] <- interpolated_data
}
Then, we look at the distribution of the variable per region. Seeing that all are skewed distributions, we decide to replace the missing values using the median by region.
question1_missing_ef_trade <- data_question1 %>%
group_by(code) %>%
mutate(PercentageMissing = mean(is.na(ef_trade))) %>% # Column % NAs
ungroup() %>%
group_by(region) %>%
filter(sum(PercentageMissing, na.rm = TRUE) > 0)
Freq_Missing_ef_trade <- ggplot(data = question1_missing_ef_trade) +
geom_histogram(aes(x = ef_trade,
fill = cut(PercentageMissing,
breaks = c(0, 0.1, 0.2, 0.3, 1),
labels = c("0-10%", "10-20%", "20-30%", "30-100%"))),
bins = 30) +
labs(title = "Distribution of economic freedom: trade", x = "ef_trade", y = "Frequency") +
scale_fill_manual(values = c("0-10%" = "blue", "10-20%" = "green", "20-30%"="red","30-100%" = "black"), labels = c("0-10%", "10-20%", "20-30%","30-100%")) +
guides(fill = guide_legend(title = "% missings")) +
facet_wrap(~ region, nrow = 2)
print(Freq_Missing_ef_trade)
data_question1 <- data_question1 %>%
group_by(code) %>%
mutate(
PercentageMissingByCode = mean(is.na(ef_trade))
) %>%
ungroup() %>%
group_by(region) %>%
mutate(
MedianByRegion = median(ef_trade, na.rm = TRUE),
ef_trade = ifelse(
PercentageMissingByCode < 0.3 & !is.na(ef_trade),
ef_trade,
ifelse(PercentageMissingByCode < 0.3, MedianByRegion, ef_trade)
)
) %>%
select(-PercentageMissingByCode, -MedianByRegion)
Economic freedom: regulation
12 countries have missing values, but the percentage of missing values is always below 25%.
question1_missing_ef_regulation <- data_question1 %>%
group_by(code) %>%
summarize(Na_ef_regulation = mean(is.na(ef_regulation)))%>% # Column % NAs
filter(Na_ef_regulation != 0)We look at the evolution of the variable over time. For the countries where this evolution is linear, we fill in the missing values using linear interpolation.
question1_missing_ef_regulation <- data_question1 %>%
group_by(code) %>%
mutate(PercentageMissing = mean(is.na(ef_regulation))) %>%
filter(code %in% question1_missing_ef_regulation$code)
Evol_Missing_ef_regulation <- ggplot(data = question1_missing_ef_regulation) +
geom_line(aes(x = year, y = ef_regulation,
color = cut(PercentageMissing,
breaks = c(0, 0.1, 0.2, 0.3, 1),
labels = c("0-10%", "10-20%", "20-30%", "30-100%")))) +
labs(title = "Evolution of economic freedom: regulation over time", x = "Years from 2000 to 2022", y = "ef_regulation") +
scale_color_manual(values = c("0-10%" = "blue", "10-20%" = "green", "20-30%" = "red", "30-100%" = "black"),
labels = c("0-10%", "10-20%", "20-30%", "50-100%")) +
guides(color = guide_legend(title = "% missings")) +
scale_x_continuous(breaks = NULL)+
facet_wrap(~ code, nrow = 2)
print(Evol_Missing_ef_regulation)
list_code <- c("ETH", "KAZ", "MDA", "SRB")
for (i in list_code) {
country_data <- data_question1 %>% filter(code == i)
interpolated_data <- na.interp(country_data$ef_regulation)
data_question1[data_question1$code == i, "ef_regulation"] <- interpolated_data
}
Then, we look at the distribution of the variable per region. Seeing that all are skewed distributions, we decide to replace the missing values using the median by region.
question1_missing_ef_regulation <- data_question1 %>%
group_by(code) %>%
mutate(PercentageMissing = mean(is.na(ef_regulation))) %>% # Column % NAs
ungroup() %>%
group_by(region) %>%
filter(sum(PercentageMissing, na.rm = TRUE) > 0)
Freq_Missing_ef_regulation <- ggplot(data = question1_missing_ef_regulation) +
geom_histogram(aes(x = ef_regulation,
fill = cut(PercentageMissing,
breaks = c(0, 0.1, 0.2, 0.3, 1),
labels = c("0-10%", "10-20%", "20-30%", "30-100%"))),
bins = 30) +
labs(title = "Distribution of economic freedom: regulation", x = "ef_regulation", y = "Frequency") +
scale_fill_manual(values = c("0-10%" = "blue", "10-20%" = "green", "20-30%"="red","30-100%" = "black"), labels = c("0-10%", "10-20%", "20-30%","30-100%")) +
guides(fill = guide_legend(title = "% missings")) +
facet_wrap(~ region, nrow = 1)
print(Freq_Missing_ef_regulation)
data_question1 <- data_question1 %>%
group_by(code) %>%
mutate(
PercentageMissingByCode = mean(is.na(ef_regulation))
) %>%
ungroup() %>%
group_by(region) %>%
mutate(
MedianByRegion = median(ef_regulation, na.rm = TRUE),
ef_regulation = ifelse(
PercentageMissingByCode < 0.3 & !is.na(ef_regulation),
ef_regulation,
ifelse(PercentageMissingByCode < 0.3, MedianByRegion, ef_regulation)
)
) %>%
select(-PercentageMissingByCode, -MedianByRegion) %>%
ungroup()
Now, we notice that there were only missing values for goals 1 and 10. As we did before, we have started to investigate where are located the NAs in our dataset for first goal1, then goal 10.
na_count <- sapply(data_question1, function(x) sum(is.na(x)))
na_count_df <- data.frame(variable = names(na_count), num_missing = na_count)
na_count_df_filtered <- subset(na_count_df, num_missing > 0)
ggplot(na_count_df_filtered, aes(x= num_missing,y=variable, fill = num_missing)) +
geom_bar(aes(fill = num_missing), stat = "identity", width = 0.8, fill = 'lightblue') +
geom_text(aes(label = num_missing), vjust = 0.5,hjust = 1.1, position = position_dodge(width = 0.9)) +
theme_minimal() +
theme(axis.text.y = element_text(hjust = 1, size=10 ),
legend.text = element_text(size = 8),
legend.title = element_text(size = 10)) +
labs(title = "Number of remaining missing values per variable ",
x = "Number of NAs",
y = "Variables")
# goal1
question1_missing_goal1 <- data_question1 %>%
group_by(code) %>%
summarize(Na_goal1 = mean(is.na(goal1)))%>%
filter(Na_goal1 != 0)
data_question1 <- data_question1 %>% filter(!code %in% question1_missing_goal1$code)
# Update List of countries deleted
list_country_deleted <- c(list_country_deleted, "KWT","NZL","OMN","SGP","UKR")
# still 42 NA values goal10
We had found that the missing values were located in only 5 countries. So we have decided to get rid of them. At this stage, there were only 42 remaining missing values. Then, same step for goal 10.
#goal10
question1_missing_goal10 <- data_question1 %>%
group_by(code) %>%
summarize(Na_goal10 = mean(is.na(goal10)))%>%
filter(Na_goal10 != 0)
data_question1 <- data_question1 %>% filter(!code %in% question1_missing_goal10$code)
# Update List of countries deleted
list_country_deleted <- c(list_country_deleted, "GUY","TTO")We have found the 2 lasts contries containing missing values. Now, our dataset is completely clean and ready to be used for our question 1.
Data for question 2 and 4
We create a column with the number of missing values by country over all the variables, except goal 1 and goal 10 that we already discussed. Since there are no other missing values, we stop here.
see_missing24 <- data_question24 %>%
group_by(code) %>%
summarise(across(everything(), ~ sum(is.na(.))) %>%
mutate(num_missing = rowSums(across(everything()))) %>%
filter(num_missing > 0))Data for question 3
We create a column with the number of missing values by country over all the variables, except goal 1 and goal 10 that we already discussed. Since there are no other missing values, we stop here.
Disasters
We begin by visualizing the missing values.
variable_names <- names(data_question3_1)
missing_percentages <- sapply(data_question3_1, function(col) mean(is.na(col)) * 100)
missing_data_summary <- data.frame(
Variable = variable_names,
Missing_Percentage = missing_percentages
)
missing_data_summary <- missing_data_summary %>%
mutate(VariableGroup = ifelse(startsWith(Variable, "goal") & Missing_Percentage == 0, "Goals without NAs", as.character(Variable)))
ggplot(data = missing_data_summary, aes(x = reorder(VariableGroup, Missing_Percentage), y = Missing_Percentage, fill = Missing_Percentage)) +
geom_bar(stat = "identity") +
geom_text(aes(label = ifelse(Missing_Percentage > 1, sprintf("%.1f%%", Missing_Percentage), ""),
y = Missing_Percentage),
position = position_stack(vjust = 1), # Adjust vertical position
color = "white", # Text color
size = 3, # Text size
hjust = 1.05) +
labs(title = "Percentage of Missing Values by Variable",
x = "Variable",
y = "Missing Percentage") +
theme_minimal() +
theme(axis.text.y = element_text(hjust = 1)) +
coord_flip()
We create a column with the number of missing values by country over all the variables, except goal 1 and goal 10 that we already discussed. We find out that there are many missing values and here are the first few lines identifying them by country.
see_missing3_1 <- data_question3_1 %>%
group_by(code) %>%
summarise(across(-c(goal1, goal10), # Exclude columns "goal1" and "goal10"
~ sum(is.na(.))) %>%
mutate(num_missing = rowSums(across(everything()))) %>%
filter(num_missing > 0))
for_kable <- head(see_missing3_1, 10)
kable(for_kable)| code | year | country | continent | region | overallscore | goal2 | goal3 | goal4 | goal5 | goal6 | goal7 | goal8 | goal9 | goal11 | goal12 | goal13 | goal15 | goal16 | total_deaths | no_injured | no_affected | no_homeless | total_affected | total_damages | num_missing |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| AGO | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 6 |
| ALB | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 9 | 9 | 9 | 9 | 9 | 9 | 54 |
| ARE | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 21 | 21 | 21 | 21 | 21 | 21 | 126 |
| ARM | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 15 | 15 | 15 | 15 | 15 | 15 | 90 |
| AUT | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 | 8 | 8 | 8 | 8 | 8 | 48 |
| AZE | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 17 | 17 | 17 | 17 | 17 | 17 | 102 |
| BDI | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 3 | 3 | 3 | 3 | 3 | 18 |
| BEL | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 5 | 5 | 5 | 5 | 5 | 30 |
| BEN | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 | 7 | 7 | 7 | 7 | 7 | 42 |
| BFA | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 5 | 5 | 5 | 5 | 5 | 30 |
In this particular case, even if there are many missing values in our disaster dataset, we made the hypothesis that disaster events can not happen every year for every country given that these are uncontrollable and non-recurring events. Therefore the NAs that we encounter will become zeroes, implying that there have been no climatic disasters.
data_question3_1[is.na(data_question3_1)] <- 0COVID19
We create a column with the number of missing values by country over all the variables, except goal 1 and goal 10 that we already discussed. Since there are no other missing values, we stop here.
see_missing3_2 <- data_question3_2 %>%
group_by(code) %>%
summarise(across(-c(goal1, goal10), # Exclude columns "goal1" and "goal10"
~ sum(is.na(.))) %>%
mutate(num_missing = rowSums(across(everything()))) %>%
filter(num_missing > 0))Conflicts
We create a column with the number of missing values by country over all the variables, except goal 1 and goal 10 that we already discussed.Two countries have missing values, we remove them (MNE and SRB).
see_missing3_3 <- data_question3_3 %>%
group_by(code) %>%
summarise(across(-c(goal1, goal10), # Exclude columns "goal1" and "goal10"
~ sum(is.na(.))) %>%
mutate(num_missing = rowSums(across(everything()))) %>%
filter(num_missing > 0))
data_question3_3 <- data_question3_3 %>% filter(!code %in% c("MNE","SRB"))
##### EXPORT as CSV #####
write.csv(data_question1, file = here("scripts","data","data_question1.csv"))
write.csv(data_question24, file = here("scripts","data","data_question24.csv"))
write.csv(data_question3_1, file = here("scripts","data","data_question3_1.csv"))
write.csv(data_question3_2, file = here("scripts","data","data_question3_2.csv"))
write.csv(data_question3_3, file = here("scripts","data","data_question3_3.csv"))